GPress: a framework for querying general feature format (GFF) files and expression files in a compressed form.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Genomic sequencing data are commonly summarized using formats like General Feature Format (GFF), Gene Transfer Format (GTF), and GFF3.
• Existing tools for accessing these annotation files often lead to substantial increases in storage space.
• Efficient data compression and retrieval are crucial for managing large-scale genomic datasets.

Purpose of the Study:

• To develop GPress, a novel framework for querying compressed General Feature Format (GFF) files.
• To enable simultaneous querying of both GFF annotation and RNA-Seq expression data.
• To reduce the storage footprint of genomic and expression datasets while maintaining fast data retrieval.

Main Methods:

• GPress applies data transformations and compresses files using the BSC (Burrows-Wheeler Transform) lossless compressor.
• It compresses data in blocks and generates index tables for rapid access.
• The framework integrates and compresses expression files from bulk and single-cell RNA-Seq experiments.

Main Results:

• GPress achieved an average size reduction of 61% compared to gzip for GFF files.
• It retrieves annotations within seconds, outperforming other methods in compression efficiency.
• When incorporating expression data, GPress reduced file sizes by over 68% compared to gzip, with retrieval times in seconds.

Conclusions:

• GPress offers a highly effective solution for compressing and querying genomic annotation and expression data.
• The framework provides significant storage savings without compromising data accessibility.
• GPress is a valuable tool for researchers managing large biological datasets, improving efficiency in genomic data analysis.